A Review of Computer Vision for Railways

Railway Computer Vision; Unmanned Aerial Vehicles UAV Drone; optical-InSAR Satellite; YOLO R-CNN; Hyperspectral-Multispectral Imaging; Overhead Catenary Computer Vision; Vehicle-borne Camera Inspection

Abstract :

[en] Modern railways continue to strive for remote and automated methods to improve the visual inspection procedures for their assets. In some cases, these inspections provide new information that could not previously be collected, while in other cases they help them improve upon the quality control, safety, time and costs associated with manual inspection. As such, computer vision continues to find applications for visually inspecting the track, earthworks, tunnels, overhead line equipment and rolling stock. Considering the recent pace of computer vision related developments, this paper seeks to review the state of the art of the field for railways. First, the hardware and data requirements are discussed, focusing on the unique challenges associated with operating optical equipment in a railway environment, such as contamination, power sources and lighting. This also discusses the most common mounting arrangements for camera hardware, including rolling-stock, satellites and way-side cameras. Next, image processing algorithms are discussed, comparing classical approaches and more modern artificial intelligence approaches, for example You Only Look Once (YOLO) and Region-Based Convolutional Neural Network (R-CNN). Then the most common applications for computer vision in the rail industry are analysed. First the track is studied considering computer vision analysis for the detection of different types of rail surface defects on plain line and turnouts, fastener defects, concrete track slab cracking and ballast particle characterisation. Next, the overhead line equipment is considered with applications related to detecting contact loss between pantograph and contact wire, stagger behaviour and defective catenary components. This is followed by discussion of other applications such as rail tunnelling subsidence, tunnel inspection, level crossings, trespass and on-track safety hazards. Finally, opportunities for future research are discussed such as hyperspectral imaging and generative AI, along with possible frontier technologies such as quantum computing.

Disciplines :

Civil engineering

Author, co-author :

OLIVIER, Bryan ; Université de Mons - UMONS > Faculté Polytechnique > Service de Mécanique rationnelle, Dynamique et Vibrations

Guo, Feng ; School of Qilu Transportation, Shandong University, Jinan, Shandong, China

Qian, Yu ; Department of Civil and Environmental Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA

Connolly, David P. ; School of Civil Engineering, University of Leeds, Leeds, U.K.

Language :

English

Title :

A Review of Computer Vision for Railways

Publication date :

2025

Journal title :

IEEE Transactions on Intelligent Transportation Systems

ISSN :

1524-9050

eISSN :

1558-0016

Publisher :

Institute of Electrical and Electronics Engineers (IEEE)

Pages :

1-32

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx8/6979/4358928/11024054.pdf?arnumber=11024054

Research unit :

F703 - Mécanique rationnelle, Dynamique et Vibrations

Research institute :

R500 - Institut des Sciences et du Management des Risques

Funders :

Natural Science Foundation of China
Natural Science Foundation of Shandong Province
China Postdoctoral Science Foundation
Belgian Funding Association (Fonds de la Recherche Scientifique—FNRS) as well as the University of Mons (Fonds Franeau) through the University of Leeds

Available on ORBi UMONS :

since 06 June 2025

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